| 基于小波分析和自相关计算的非接触式生理信号检测 |
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| 引用本文： | 刘璐瑶,张森,肖文栋. 基于小波分析和自相关计算的非接触式生理信号检测[J]. 工程科学学报, 2021, 43(9): 1206-1214. DOI: 10.13374/j.issn2095-9389.2021.01.13.001 |
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| 作者姓名： | 刘璐瑶 张森 肖文栋 |
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| 作者单位： | 1.北京科技大学自动化学院，北京 100083 |
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| 基金项目： | 国家重点研发计划课题资助项目（2017YFB1401203）；佛山市科技创新专项资助项目（BK20AF005） |
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| 摘 要： | 采用调频连续波（Frequency modulated continuous wave, FMCW）雷达实现非接触式生理信号检测，并提出了基于小波分析和自相关计算（Wavelet analysis and autocorrelation computation, WAAC）的检测方法。首先，毫米波FMCW雷达发射电磁波信号，并接收来自身体的反射信号。然后，通过信号预处理从中频信号中提取包含呼吸和心跳的相位信息，消除直流偏置并完成相位解缠。最后，基于小波包分解（Wavelet packet decomposition, WPD）从原始信号中得到心跳和呼吸信号，利用自相关计算减小杂波对心跳信号的影响，进而提取高精度的心率参数。应用FMCW雷达对10名受试者进行实验测试，结果表明本文方法得到的呼吸和心率的平均绝对误差率平均值分别小于1.65%和1.83%。
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| 关 键 词： | 非接触式生理信号检测 心跳检测 呼吸检测 小波分析 自相关计算 调频连续波雷达 |
| 收稿时间： | 2021-01-13 |
Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation |
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| LIU Lu-yao,ZHANG Sen,XIAO Wen-dong. Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation[J]. Chinese Journal of Engineering, 2021, 43(9): 1206-1214. DOI: 10.13374/j.issn2095-9389.2021.01.13.001 |
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| Authors: | LIU Lu-yao ZHANG Sen XIAO Wen-dong |
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| Affiliation: | 1.School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China2.Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China3.Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China |
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| Abstract: | Vital signs are important parameters for human health status assessment, and timely, accurate detection is of great significance for modern health care and intelligent medical applications. Detecting vital signs, such as heartbeat and respiration signals, provides a variety of diseases with reliable diagnosis and effective prevention. Conventional contact detection may restrict the behaviors of users, cause additional burdens, and render users uncomfortable. In recent years, noncontact detection technology has successfully achieved remote long-term detection for respiration and heartbeat signals. Compared to conventional contact-detection approaches, noncontact heartbeat and respiration detection using a millimeter-wave radar is preferable as it causes no disturbance to the subject, bringing a comfortable experience, and detects vital signs under natural conditions. However, noncontact vital signs detection is challenging owing to environmental noise. Especially, heartbeat signals are very weak and are merged with respiration harmonics and environmental noise, and their extraction and recognition are even more difficult. This paper applied a frequency-modulated continuous wave (FMCW) radar to detect vital signs. The study also presented a noncontact heartbeat and respiration signals detection approach based on wavelet analysis and autocorrelation computation (WAAC). The millimeter-wave FMCW radar first transmited the electromagnetic signal and received the reflected echo signals from the human body. Thereafter, the phase information of the intermediate frequency signals was extracted, which included respiration and heartbeat signals. The direct current offset of the phase information was corrected, and the phase was unwrapped. Finally, the wavelet packet decomposition was used to reconstruct heartbeat and respiration signals from the original signal, and an autocorrelation computation was utilized to reduce the effect of clutters on the heart rate detection. Experiments were conducted on ten subjects. Results show that the average absolute error percentage of WAAC is less than 1.65% and 1.83% for respiration and heartbeat rates, respectively. |
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